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Review
. 2024 Nov 1;21(6):061001.
doi: 10.1088/1741-2552/ad8a8e.

Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide

Miles Wischnewski  1   2 , Sina Shirinpour  2 , Ivan Alekseichuk  2   3 , Maria I Lapid  4 , Ziad Nahas  5 , Kelvin O Lim  5 , Paul E Croarkin  4 , Alexander Opitz  2
Affiliations
Review

Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide

Miles Wischnewski et al. J Neural Eng. .

Abstract

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke.

Keywords: brain states; closed-loop neuromodulation; electroencephalography; precision treatment; transcranial magnetic stimulation.

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Conflict of interest statement

M W and I A received funding from the Brain and Behavior Research Foundation. A O received funding from the University of Minnesota MnDRIVE Initiative, NSF Career Awards 2143852 and NIH R01MH136053. A O and P E C received funding from the Minnesota Partnership for Biotechnology & Medical Genomics. Further, A O is an inventor on patents and patent applications describing methods and devices for non-invasive brain stimulation. PEC has received in-kind support (equipment) for investigator-initiated research studies from Neuronetics, Inc., and MagVenture, Inc. He has received research grant funding from Neuronetics, Inc. and NeoSync, Inc. The other authors do not report any conflict of interest.

Figures

Figure 1.
Figure 1.
Summary of studies on closed-loop TMS-EEG over primary motor cortex targeting phases of the sensorimotor mu rhythm. Effect sizes represent the effect of the target phase on motor-evoked potential amplitude, normalized to the grand average and expressed in Hedges’ g. On the x-axis the phase of the mu rhythm is represented that was targeted by TMS. Note the inverse relationship between the mu phase and MEP amplitude, with the largest responses at the trough and the smallest responses at the peak phase.
Figure 2.
Figure 2.
Six factors that are crucial for successful closed-loop experiments, namely, (A) the hardware processing and transmission delays, (B) extraction of the signal of interest using frequency and spatial filtering, (C) defining the neuromarker of interest in terms of frequency band, power, and phase of the brain rhythm, (D) requirements for signal quality for the algorithm of choice, (E) the algorithm for closed-loop processing, (F) strategy for operations in the presence of strong extracephalic or technical noise.
See this image and copyright information in PMC

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